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Space-Varying Iterative Restoration of Diffuse Optical Tomograms Reconstructed by the Photon Average Trajectories Method

Abstract

The possibility of improving the spatial resolution of diffuse optical tomograms reconstructed by the photon average trajectories (PAT) method is substantiated. The PAT method recently presented by us is based on a concept of an average statistical trajectory for transfer of light energy, the photon average trajectory (PAT). The inverse problem of diffuse optical tomography is reduced to a solution of an integral equation with integration along a conditional PAT. As a result, the conventional algorithms of projection computed tomography can be used for fast reconstruction of diffuse optical images. The shortcoming of the PAT method is that it reconstructs the images blurred due to averaging over spatial distributions of photons which form the signal measured by the receiver. To improve the resolution, we apply a spatially variant blur model based on an interpolation of the spatially invariant point spread functions simulated for the different small subregions of the image domain. Two iterative algorithms for solving a system of linear algebraic equations, the conjugate gradient algorithm for least squares problem and the modified residual norm steepest descent algorithm, are used for deblurring. It is shown that a gain in spatial resolution can be obtained.

References

  1. Arridge SR: Optical tomography in medical imaging. Inverse Problems 1999,15(2):R41-R93. 10.1088/0266-5611/15/2/022

    Article  MathSciNet  MATH  Google Scholar 

  2. Yodh A, Chance B: Spectroscopy and imaging with diffusing light. Physics Today 1995,48(3):34-40. 10.1063/1.881445

    Article  Google Scholar 

  3. Hielscher AH, Klose AD, Hanson KM: Gradient-based iterative image reconstruction scheme for time-resolved optical tomography. IEEE Transactions on Medical Imaging 1999,18(3):262-271. 10.1109/42.764902

    Article  Google Scholar 

  4. Lyubimov VV: Physical foundations of the strongly scattering media laser tomography. Laser Optics '95: Biomedical Applications of Lasers, June 1996, St. Petersburg, Russia, Proceedings of SPIE 2769: 107–110.

    Article  Google Scholar 

  5. Lyubimov VV: Optical tomography of highly scattering media by using the first transmitted photons of ultrashort pulses. Optics and Spectroscopy 1996,80(4):616-619.

    Google Scholar 

  6. Lyubimov VV, Mironov EP, Murzin AG, Volkonsky VB, Kravtsenyuk OV: Calculation of shadows induced by macroinhomogeneities located inside a strongly scattering object using the integration over the average photon path. Photon Propagation in Tissues III, September 1997, San Remo, Italy, Proceedings of SPIE 3194: 409–416.

    Article  Google Scholar 

  7. Lyubimov VV: On the spatial resolution of optical tomography of strongly scattering media with the use of the directly passing photons. Optics and Spectroscopy 1999,86(2):251-252.

    Google Scholar 

  8. Volkonskii VB, Kravtsenyuk OV, Lyubimov VV, Mironov EP, Murzin AG: The use of statistical characteristics of photon trajectories for the tomographic studies of optical macroheterogeneities in strongly scattering objects. Optics and Spectroscopy 1999,86(2):253-260.

    Google Scholar 

  9. Kravtsenyuk OV, Lyubimov VV: Specific features of statistical characteristics of photon trajectories in a strongly scattering medium near an object surface. Optics and Spectroscopy 2000,88(4):608-614. 10.1134/1.626846

    Article  Google Scholar 

  10. Kravtsenyuk OV, Lyubimov VV: Application of the method of smooth perturbations to the solution of problems of optical tomography of strongly scattering objects containing absorbing macroinhomogeneities. Optics and Spectroscopy 2000,89(1):107-112. 10.1134/1.1131523

    Article  Google Scholar 

  11. Lyubimov VV, Kalintsev AG, Konovalov AB, et al.: Application of the photon average trajectories method to real-time reconstruction of tissue inhomogeneities in diffuse optical tomography of strongly scattering media. Physics in Medicine and Biology 2002,47(12):2109-2128. 10.1088/0031-9155/47/12/308

    Article  Google Scholar 

  12. Lyubimov VV, Konovalov AB, Kutuzov II, et al.: Influence of fast reconstruction algorithms on spatial resolution of optical diffuse tomography by photon average trajectories method. Saratov Fall Meeting 2001: Optical Technologies in Biophysics and Medicine III, October 2002, Saratov, Russia, Proceedings of SPIE 4707: 53–59.

    Google Scholar 

  13. Konovalov AB, Lyubimov VV, Kutuzov II, et al.: Application of integral transform algorithms to high-resolution reconstruction of tissue inhomogeneities in medical diffuse optical tomography. Optics in Health Care and Biomedical Optics: Diagnostics and Treatment, October 2002, Shanghai, China, Proceedings of SPIE 4916: 9–21.

    Google Scholar 

  14. Konovalov AB, Lyubimov VV, Kutuzov II, et al.: Application of transform algorithms to high-resolution image reconstruction in optical diffusion tomography of strongly scattering media. Journal of Electronic Imaging 2003,12(4):602-612. 10.1117/1.1604119

    Article  Google Scholar 

  15. Lyubimov VV, Kravtsenyuk OV, Kalintsev AG, et al.: The possibility of increasing the spatial resolution in diffusion optical tomography. Journal of Optical Technology 2003,70(10):715-720. 10.1364/JOT.70.000715

    Article  Google Scholar 

  16. Golubkina OV, Kalintsev AG, Konovalov AB, et al.: Application of photon average trajectories method for separate mapping of absorbing and scattering macroinhomogeneities using time-domain measurements technique. Photon Migration, Optical Coherence Tomography, and Microscopy, June 2001, Munich, Germany, Proceedings of SPIE 4431: 275–281.

    Article  Google Scholar 

  17. Nagy JG, Palmer K, Perrone L: Iterative methods for image deblurring: a Matlab object-oriented approach. Numerical Algorithms 2004,36(1):73-93.

    Article  MathSciNet  MATH  Google Scholar 

  18. Nagy JG, O'Leary DP: Restoring images degraded by spatially variant blur. SIAM Journal of Scientific Computing 1998,19(4):1063-1082. 10.1137/S106482759528507X

    Article  MathSciNet  MATH  Google Scholar 

  19. Nagy JG, O'Leary DP: Fast iterative image restoration with a spatially-varying PSF. Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, July 1997, San Diego, Calif, USA , Proceedings of SPIE 3162: 388–399.

    Google Scholar 

  20. Björck Å: Numerical Methods for Least Squares Problems. SIAM, Philadelphia, Pa, USA; 1996.

    Book  MATH  Google Scholar 

  21. Kaufman L: Maximum likelihood, least squares, and penalized least squares for PET. IEEE Transactions on Medical Imaging 1993,12(2):200-214. 10.1109/42.232249

    Article  Google Scholar 

  22. Nagy JG, Strakos Z: Enforcing nonnegativity in image reconstruction algorithms. Mathematical Modeling, Estimation, and Imaging, July 2000, San Diego, Calif, USA, Proceedings of SPIE 4121: 182–190.

    Article  Google Scholar 

  23. Schweiger M, Arridge SR, Hiraoka M, Delpy DT: The finite element method for the propagation of light in scattering media: boundary and source conditions. Medical Physics 1995,22(11):1779-1792. 10.1118/1.597634

    Article  Google Scholar 

  24. Sandwell DT: Biharmonic spline interpolation of GEOS-3 and SEASAT altimeter data. Geophysical Research Letters 1987,14(2):139-142. 10.1029/GL014i002p00139

    Article  Google Scholar 

  25. Kak AC, Slaney M: Principles of Computerized Tomographic Imaging. IEEE Press, New York, NY, USA; 1988.

    MATH  Google Scholar 

  26. The Math Work : Using Matlab, Version 6. 2000.

    Google Scholar 

  27. Papoulis A: Systems and Transforms with Applications in Optics. McGraw-Hills, New York, NY, USA; 1968.

    Google Scholar 

  28. Feng S, Zeng F, Chance B: Monte Carlo simulations of photon migration path distributions in multiple scattering media. Photon Migration and Imaging in Random Media and Tissues, January 1993, Los Angeles, Calif, USA, Proceedings of SPIE 1888: 78–89.

    Article  Google Scholar 

  29. McNown SR, Jain AK: Approximate shift-invariance by warping shift-variant systems. In The Restoration of HST Images and Spectra II. Edited by: Hanisch RJ, White RL. Space Telescope Science Institute, Baltimore, Md, USA; 1994:181-187.

    Google Scholar 

  30. Robbins GM, Huang TS: Inverse filtering for linear shift-variant imaging systems. Proceedings of the IEEE 1972,60(7):862-872.

    Article  Google Scholar 

  31. Sawchuk AA: Space-variant image restoration by coordinate transformations. Journal of the Optical Society of America 1974,64(2):138-144. 10.1364/JOSA.64.000138

    Article  Google Scholar 

  32. Adorf H-M: Towards HST restoration with space-variant PSF, cosmic rays and other missing data. In The Restoration of HST Images and Spectra II. Edited by: Hanisch RJ, White RL. Space Telescope Science Institute, Baltimore, Md, USA; 1994:72-78.

    Google Scholar 

  33. Trussell HJ, Fogel S: Identification and restoration of spatially variant motion blurs in sequential images. IEEE Transactions on Image Processing 1992,1(1):123-126. 10.1109/83.128039

    Article  Google Scholar 

  34. Fish DA, Grochmalicki J, Pike ER: Scanning singular-value-decomposition method for restoration of images with space-variant blur. Journal of the Optical Society of America A: Optics, Image Science, and Vision 1996,13(3):464-469. 10.1364/JOSAA.13.000464

    Article  Google Scholar 

  35. Kamm J, Nagy JG: Kronecker product and SVD approximations in image restoration. Linear Algebra and Its Applications 1998,284(1–3):177-192.

    Article  MathSciNet  MATH  Google Scholar 

  36. Restore Tools: An Object Oriented Matlab Package for Image Restoration 2002.https://doi.org/www.mathcs.emory.edu/~nagy/RestoreTools

  37. Hanke M: Conjugate Gradient Type Methods for Ill-Posed Problems, Pitman Research Notes in Mathematics. Longman Scientific & Technical, Harlow, UK; 1995.

    MATH  Google Scholar 

  38. Nagy JG, Palmer KM: Steepest descent, CG, and iterative regularization of ill-posed problems. BIT Numerical Mathematics 2003,43(5):1003-1017.

    Article  MathSciNet  MATH  Google Scholar 

  39. Vogel CR: Computational Methods for Inverse Problems. SIAM, Philadelphia, Pa, USA; 2002.

    Book  MATH  Google Scholar 

  40. Bertero M, Boccacci P: Introduction to Inverse Problems in Imaging. IOP, London, UK; 1998.

    Book  MATH  Google Scholar 

  41. Richardson WH: Bayesian-based iterative method of image restoration. Journal of the Optical Society of America 1972,62(1):55-59. 10.1364/JOSA.62.000055

    Article  MathSciNet  Google Scholar 

  42. Lucy LB: An iterative technique for the rectification of observed distributions. The Astronomical Journal 1974,79(6):745-753.

    Article  Google Scholar 

  43. Golub GH, van Loan CF: Matrix Computations. 3rd edition. John Hopkins Institute Press, Baltimore, Md, USA; 1989.

    MATH  Google Scholar 

  44. Tsui BMW, Zhao X, Frey EC, Gullberg GT: Comparison between ML-EM and WLS-CG algorithms for SPECT image reconstruction. IEEE Transactions on Nuclear Science 1991,38(6, part 2):1766-1772.

    Article  Google Scholar 

  45. Jiang M, Wang G, Skinner MW, Rubinstein JT, Vannier MW: Blind deblurring of spiral CT images. IEEE Transactions on Medical Imaging 2003,22(7):837-845. 10.1109/TMI.2003.815075

    Article  Google Scholar 

  46. Groetsch CW: The Theory of Tikhonov Regularization for Fredholm Integral Equations of the First Kind. Pitman, Boston, Mass, USA; 1984.

    MATH  Google Scholar 

  47. Hansen PC, O'Leary DP: The use of the L-curve in the regularization of discrete ill-posed problems. SIAM Journal on Scientific Computing 1993,14(6):1487-1503. 10.1137/0914086

    Article  MathSciNet  MATH  Google Scholar 

  48. Kilmer ME, O'Leary DP: Choosing regularization parameters in iterative methods for ill-posed problems. SIAM Journal on Matrix Analysis and Applications 2001,22(4):1204-1221. 10.1137/S0895479899345960

    Article  MathSciNet  MATH  Google Scholar 

  49. Calvetti D, Landi G, Reichel L, Sgallari F: Non-negativity and iterative methods for ill-posed problems. Inverse Problems 2004,20(6):1747-1758. 10.1088/0266-5611/20/6/003

    Article  MathSciNet  MATH  Google Scholar 

  50. Dehghani H, Pogue BW, Poplack SP, Paulsen KD: Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results. Applied Optics 2003,42(1):135-145. 10.1364/AO.42.000135

    Article  Google Scholar 

  51. Li A, Miller EL, Kilmer ME, et al.: Tomographic optical breast imaging guided by three-dimensional mammography. Applied Optics 2003,42(25):5181-5190. 10.1364/AO.42.005181

    Article  Google Scholar 

  52. Hebden JC, Arridge SR, Schweiger M: Investigation of alternative data types for time resolved optical tomography. OSA Technical Digest, Biomedical Topical Meetings, 1998, Washington, DC, USA 21: 162–167.

    Google Scholar 

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Correspondence to Alexander B Konovalov.

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Konovalov, A.B., Vlasov, V.V., Kravtsenyuk, O.V. et al. Space-Varying Iterative Restoration of Diffuse Optical Tomograms Reconstructed by the Photon Average Trajectories Method. EURASIP J. Adv. Signal Process. 2007, 034747 (2007). https://doi.org/10.1155/2007/34747

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